Spatial characteristics of multi-channel source audio

ABSTRACT

In some examples, an audio control system can include a first set of resources, a second set of resources and a controller. The first set of resources can generate a frequency energy band representation of a multi-channel source audio input. Additionally, the second set of resources can determine at least a value representing a strength of correlation between multiple channels of the multi-channel source audio input. Moreover, the audio output controller can determine a set of control parameters for tuning sound creation from an audio signal generator to reflect a set of spatial characteristics of the source audio input, based on the frequency energy band representation and the first value.

BACKGROUND

Audio content is often delivered in a multi-channel format (e.g., Dolby5.1 or a 7.1 surround sound format) for output on a multi-channel soundsystem.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure herein is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings in which likereference numerals refer to similar elements, and in which:

FIG. 1A illustrates an example audio control system for reformatting amulti-channel source audio input into a multi-dimensional audio signal;

FIG. 1B illustrates another example of an audio control system forreformatting a multi-channel source audio input into a multi-dimensionalaudio signal;

FIG. 2 illustrates an example method for reformatting a multi-channelsource audio input into a multi-dimensional audio signal; and

FIG. 3 illustrates a computer system upon which aspects described hereinmay be implemented.

Throughout the drawings, identical reference numbers designate similar,but not necessarily identical elements. The figures are not necessarilyto scale, and the size of some parts may be exaggerated to more clearlyillustrate the example shown. Moreover, the drawings provide examplesand/or implementations consistent with the description. However, thedescription is not limited to the examples and/or implementationsprovided in the drawings.

DETAILED DESCRIPTION

A multi-channel source audio input can be manually reformatted orcalibrated into a multi-dimensional audio signal that, when rendered bya mono or stereo audio system, emulates a calibrated sound field arisingfrom a distributed or multi-channel sound systems that may otherwise beoptimized for the multi-channel audio source input. Examples provide foran audio control system to automate the reformatting or calibration of amulti-channel source audio input (e.g., Dolby 5.1 or a 7.1 surroundsound signal) into the multi-dimensional audio signal. The audio controlsystem can reformat a multi-channel source audio input 102 by generatinga frequency band energy representation of the multi-channel source audioinput. Additionally, the audio control system can determine a strengthof correlation of each channel of the multi-channel source audio input.Based on the frequency band energy representation and the strength ofcorrelation between each channel of the multi-channel source audioinput, the audio control system can tune an audio signal generator,using a trained machine learning model, such that sound creationrendered from the audio signal generator reflects spatialcharacteristics of the multi-channel source audio input.

As described by various examples, the audio control system can operateon a mono or stereo or even a multichannel audio system to render sound,in a manner that emulates a calibrated sound field arising from by adistributed or multi-channel sound systems that may otherwise beoptimized for the multi-channel audio source input. In such examples,the sound rendered from the audio signal generator that was tuned by theaudio control system can emulate a sound from an audio signal generatorthat was manually tuned by a human operator. Among other benefits, theaudio control system can reformat the multi-channel audio source inputinto a multi-dimensional audio signal that is faster and more efficientthan manual processes, such as a human operator manually reformatting orcalibrating the multi-channel audio source input.

Examples described herein provide that methods, techniques, and actionsperformed by a computing device are performed programmatically, or as acomputer-implemented method. Programmatically, as used, means throughthe use of code or computer-executable instructions. These instructionscan be stored in a memory resource of the computing device. Aprogrammatically performed step may or may not be automatic.

Additionally, examples described herein can be implemented usingprogrammatic modules, engines, or components. A programmatic module,engine, or component can include a program, a sub-routine, a portion ofa program, or a software component or a hardware component capable ofperforming stated tasks or functions. As used herein, a module orcomponent can exist on a hardware component independently of othermodules or components. Alternatively, a module or component can be ashared element or process of other modules, programs, or machines.

Moreover, examples described herein can utilize specialized computingdevices, including processing and memory resources. For example,examples described may be implemented, in whole or in part, on computingdevices such as servers, desktop computers, cellular or smartphones,personal digital assistants (e.g., PDAs), laptop computers, printers,digital picture frames, network equipment (e.g., routers), wearablecomputing devices, and tablet devices. Memory, processing, and networkresources may all be used in connection with the establishment, use, orperformance of any example described herein (including with theperformance of any method or with the implementation of any system). Forinstance, a computing device coupled to a data storage device storingthe computer program and configured to execute the program correspondsto a special-purpose computing device. Furthermore, any computingsystems referred to in the specification may include a single processoror may be architectures employing multiple processor designs forincreased computing capability.

Furthermore, examples described herein may be implemented through theuse of instructions that are executable by a processor. Theseinstructions may be carried on a computer-readable medium. Machinesshown or described with figures below provide examples of processingresources and computer-readable mediums on which instructions forimplementing examples described can be carried and/or executed. Inparticular, the numerous machines shown with examples described includeprocessor(s) and various forms of memory for holding data andinstructions. Examples of computer-readable mediums include permanentmemory storage devices, such as hard drives on personal computers orservers. Other examples of computer storage mediums include portablestorage units, such as CD or DVD units, flash memory (such as carried onsmartphones, multifunctional devices or tablets), and magnetic memory.Computers, terminals, network enabled devices (e.g., mobile devices,such as cell phones) are all examples of machines and devices thatutilize processors, memory, and instructions stored on computer-readablemediums. Additionally, examples may be implemented in the form ofcomputer-programs, or a computer usable carrier medium capable ofcarrying such a program.

Alternatively, examples described herein may be implemented through theuse of dedicated hardware logic circuits that are comprised of aninterconnection of logic gates. Such circuits are typically designedusing a hardware description language (HDL), such as Verilog and VHDL.These languages contain instructions that ultimately define the layoutof the circuit. However, once the circuit is fabricated, there are noinstructions. All the processing is performed by interconnected gates.

System Description

FIG. 1A illustrates an example audio control system for reformatting amulti-channel source audio input into a multi-dimensional audio signal.As illustrated in FIG. 1A, audio control system 100 can reformatmulti-channel source audio input 102 (e.g., a 5.1 or 7.1 surround soundformat) into a multi-dimensional audio signal. Multi-channel sourceaudio input 102 can be characterized by a set of spatial parameters thatoriginally is tuned for sound creation by a particular configurationand/or distribution of audio output devices (e.g., a multi-channel audiosystem). Specific examples of such spatial parameters include currentoutput, user level, limiter related parameters (e.g., parameters relatedto a predetermined threshold that the level of signal is limited to),International Telecommunication Union (ITU) out (parameters for ITUstandard surround sound output), ITU binaural (parameters for ITUstandard binaural or stereo sound output), Tomlison Holman's eXperiment(THX) (parameters for THX certified surround sound output), left/rightbinaural, side binaural, low frequency effects channel, output,multiband dynamic range compression, upfront upmix (e.g., parameters forincreasing the number of channels related to the upfront channel),center upmix (e.g., parameters for increasing the number of channelsrelated to the center channel), and surround upmix (e.g., parameters forincreasing the number of channels related to the surround channel, andshelving parameters for filters to reduce or increase above apredetermined frequency (e.g., ITU standards).

Audio control system 100 can include resources (e.g., hardwarecomponents, such as integrated circuits or specialized integratedcircuits, and/or software or logic stored on the hardware components,such as software stored on a non-transitory computer-readable medium) toreformat or adjust multi-channel source audio input 102 into amulti-dimensional audio signal that preserves the physicalcharacteristics of some or all of the spatial characteristics orparameters. For example, audio control system 100 can include a firstset of resources (energy frequency band resource (EFB) 104) to generatea frequency energy band representation of multi-channel source audioinput 102), a second set of resources (correlator resource) 106 todetermine at least a value representing a strength of correlationbetween multi-channel source audio input 102, audio output controller108 to determine a set of control parameters for tuning sound creationto reflect a set of spatial characteristics or parameters ofmulti-channel source audio input 102. As described by some examples,audio output controller 108 can determine the set of control parametersbased on the frequency energy band representation and the representativevalue of the strength of correlation between individual channels ofmulti-channel source audio input 102.

In some examples, a first set of resources (EFB resource 104) canprocess each channel of the multi-channel source audio input 102 (e.g.,surround-left channel, surround-right channel, front-left channel,front-right channel, rear-center channel and/or front-center channel) todetermine the frequency energy band representations of each channel ofmulti-channel source audio input 102 and the represented value of thestrength of correlation between each individual channels ofmulti-channel source audio input 102, respectively.

In some examples, a first set of resources (EFB resource 104) canprocess each channel of the multi-channel source audio input 102 (e.g.,surround-left channel, surround-right channel, front-left channel,front-right channel, rear-center channel and/or front-center channel) todetermine a frequency energy band representations of each channel ofmulti-channel source audio input 102. For example, the first set ofresources (EFB resource 104) can analyze single or multiple frequencybands of each channel of the multi-channel source audio input 102 toobtain an energy representation of the multi-channel source audio input.The first set of resources (EFB resource 104) can include a bank ofauditory filters. Additionally, each channel of multi-channel sourceaudio input 102 can be an input to the bank of auditory filters tooutput a set of predetermined frequencies of each channel. For example,multi-channel source audio input 102 can include a surround-left channelaudio signal and a front-left channel audio signal and EFB resource 104can include a first bank of auditory filters and a second bank ofauditory filters. EFB resource 104 can apply the first bank of auditoryfilters to the surround-left channel audio signal to output a set ofpredetermined frequencies for the surround-left channel audio signal.Additionally, EFB resource 104 can apply the second bank of auditoryfilters to the front-left channel audio signal to output a set ofpredetermined frequencies for the front-left channel audio signal. Insome examples, the first set of resources (EFB resource 104) cangenerate a frequency energy band representation of each channel ofmulti-channel source audio input 102 by utilizing the set ofpredetermined frequencies of each channel. For example, EFB resource 104can generate a frequency band representation for the left surround-leftchannel audio signal based on the set of predetermined frequencies forthe surround-left channel audio signal. Additionally, EFB resource 104can generate a frequency band representation for the front-left channelaudio signal, based on the set of predetermined frequencies for thefront-left channel audio signal.

In some examples, a second set of resources (correlator resource 106)can processing each channel of the multi-channel source audio input 102to determine a represented value of the strength of correlation betweeneach individual channels of multi-channel source audio input 102. Forexample, multi-channel source audio input can include audio signal for asurround-right channel and a front-right channel. Additionally, in suchan example, the second set of resources (correlator resource 106) candetermine the strength of correlation and a representative value of thestrength of correlation between the surround-right channel audio signaland the front-right channel audio signal.

Audio control system 100 can include audio output controller 108 todetermine a set of control parameters that can reformat multi-channelsource audio input 102. Additionally, audio output controller 108determines the set of control parameters based on a frequency energyband representation of each channel of multi-channel source audio input102 and the strength of correlation between each channel ofmulti-channel source audio input 102. In some examples audio controlsystem 100 can include hardware components (e.g., integrated circuits orspecialized integrated circuits) and/or software or logic stored on thehardware components (e.g., software stored on non-transitorycomputer-readable medium) to determine the set of control parametersthat can reformat multi-channel source audio input 102.

Audio signal generator 110 can utilize a set of control parameters fromaudio output controller 108 to tune sound creation from audio signalgenerator 110 to reflect a set of spatial characteristics ofmulti-channel source audio input 102. In some examples, audio signalgenerator 110 can include hardware components, such as integratedcircuits and/or specialized integrated circuits, and/or software orlogic stored on the hardware components (e.g., software stored onnon-transitory computer-readable medium), to tune sound creation, basedon the set of control parameters.

FIG. 1B illustrates an example of a variation of audio control systemfor reformatting a multi-channel source audio input into amulti-dimensional audio signal. Similar to FIG. 1A, FIG. 1B, illustratesaudio control system 150 that can reformat multi-channel source audioinput 180 (e.g., a 5.1 or 7.1 surround sound format) into amulti-dimensional audio signal. In some examples, multi-channel sourceaudio input 180 can include a set of spatial parameters that optimizesthe output of sound from multi-channel source audio input 180 by adistributed and/or a suitably configured audio system.

As illustrated in FIG. 1B, audio control system 100 can include a banksof auditory filters (e.g., filter 1 160 ₁, . . . , 160 _(n), filter 2162 ₁, . . . , 162 _(n), . . . , filter N 164 ₁, . . . , 164 _(n)) andcorresponding root-mean square module (e.g., RMS 1 166 ₁, . . . , 166_(n), RMS 2 168 ₁, . . . , 168 _(n), . . . , RMS N 168 ₁, . . . , RMS168 _(n)) to generate a frequency energy band representation ofmulti-channel source audio input 180, correlator resource 166 todetermine at least a value representing a strength of correlationbetween multi-channel source audio input 180, audio output controller172 to determine a set of control parameters for tuning sound creationto reflect a set of spatial characteristics of multi-channel sourceaudio input 180, and spatial audio parameter module 176 to utilize theset of control parameters to reformat multi-channel source audio input180 to a multi-dimensional audio signal.

Additionally, similar to FIG. 1A, audio control system 152 can reformatmulti-channel source audio input 180 into a multi-dimensional audiosignal by processing each channel of the multi-channel source audioinput 180 to determine a frequency energy band representation of eachchannel of multi-channel source audio input 180. Audio output controller172 can utilize the frequency energy band representation to determine aset of control parameters for tuning multi-channel source audio input180 to determine a set of control parameters for tuning sound creationto reflect a set of spatial characteristics or parameters ofmulti-channel source audio input 102.

In some examples, each channel of multi-channel source audio input 180(e.g., surround-left channel, surround-right channel, front-leftchannel, front-right channel, rear-center channel and/or front-centerchannel) can be an input to a separate bank of auditory filters (e.g., afront-center channel audio signal can be an input to filter 1 160 ₁, . .. , 160 _(n) and a front-right channel audio signal can be an input tofilter 2 162 ₁, . . . , 162 _(n)) to output a set of predeterminedfrequencies of each channel. In such examples, each bank of auditoryfilters can include a bank of band-pass filters. In such examples, thebank of band-pass filters can mimic a human auditory filter-bank, sothat the output of each bank of auditory filter mimics that how a humanear filters sound. Examples of band-pass filter banks include a ⅓-octavefilter-bank, ⅙th-octave filter-bank, 1/12-th octave filter-bank,critical band filter-bank, equivalent rectangular bandwidth, andgammatone filter-bank.

Each set of predetermined frequencies of each channel of multi-channelsource audio input 180 can be processed by a corresponding root-meansquare module (e.g., RMS 1 160 ₁, . . . , 160 _(n), RMS 1 162 ₁, . . . ,162 _(n), . . . , RMS N 164 ₁, . . . , RMS 164 _(n)) to generate afrequency energy band representation of each channel. For example, eachset of predetermined frequencies of each channel can be an input to acorresponding root-mean square module. An example of a root-mean squarefunction that the root-mean square module can utilize to generate afrequency energy band representation of each channel includes:

${rms}_{(k)} = \left( {\sqrt{\frac{1}{F}}{\sum\limits_{p = 1}^{F}\left( x_{p}^{(k)} \right)^{2}}} \right)$

where rms(k) is the energy in a frame, represented by F (e.g., 480samples/frame), and X is the output from each filter of the bank ofauditory filters represented by k=1, . . . , M.

A strength of correlation between each channel of multi-channel sourceaudio input 180 (e.g., first channel 154, second channel 156 . . . Nchannel 158) can be utilized by audio output controller 172 to determinea set of control parameters that can reformat multi-channel source audioinput 180. For example, correlator resource 166 can obtain multi-channelsource audio signal input 180 that includes first channel 154 and secondchannel 156. Correlator resource 166 can determine a strength ofcorrelation between first channel 154 and second channel 156. In someexamples, correlator resource 166 can utilize the following function todetermine the strength of correlation between the front right channeland the rear center channel, as well as any other channels included inmulti-channel source audio signal 180.

${r_{i,j}(l)} = {\sum\limits_{p = 1}^{F}{{x_{i}(p)}{x_{j}\left( {l + p} \right)}}}$

where r_(i,j)(l) is the strength of correlation, F is the frame, l isthe lag and x_(i) and x_(j) are the channels being compared.

Additionally, correlator resource 166 can determine a set ofdecorrelation parameters for channels of multi-channel source audioinput 180 that have a high degree of similarity or have a high strengthof correlation. In some examples, audio output controller 172 can alsoutilize the decorrelation parameters to determine a set of controlparameters that can reformat multi-channel source audio input 180. Thestrength of correlation between different channels of multi-channelsource audio input 180 can indicate the degree of similarity betweenaudio signals of the different channels. The greater the degree ofsimilarity between the different channels or the heavier the correlationbetween the different channels can indicate a strong monoaural virtualsource or phantom source. For example, a mono or stereo audio system canoutput multi-channel source audio input 180 with channels with audiosignals that have a high degree of similarity with one another. As such,the mono or stereo audio system can output audio with a phantom sourcethat can be perceived as being directly in front of a listener asopposed to the intended surround sound characteristics of multi-channelsource audio input 180.

In some examples, correlator resource 166 can base a set ofdecorrelation parameters on an all-pass filter. All-pass filters can beapplied to channels of multi-channel source audio input 180 that have ahigh strength in correlation to decorrelate them. The stronger thestrength in correlation, the higher the order of the all-pass filterthat may be applied to decorrelate highly correlated channels ofmulti-channel source audio input 180. In some examples, correlatorresource 166 can base the decorrelation parameters on the followingexample all-pass filter pair H(z) and {tilde over (H)}(z) of order N andpole-coefficient λ to decorrelate such channels.

${H(z)} = \frac{z^{- N} - \lambda}{1 - {\lambda^{*}z^{- N}}}$${\overset{\sim}{H}(z)} = \frac{z^{- N} + \lambda}{1 + {\lambda^{*}z^{- N}}}$

In some examples, audio output controller 172 can determine the set ofdecorrelation parameters on the all-pass filter, based on the strengthof correlation determined by correlator resource 166.

In some examples, correlator resource 166 can determine a strength ofcorrelation between each channel of multi-channel source audio input 102and a Kronecker delta function (δ(n)), by using an Euclidean norm(φ=∥r_(i,j)(n)−δ(n−F)∥2). Additionally, correlator resource 166 can alsoutilize the Euclidean norm as a scalar input to determine the strengthof correlation between each channel of multi-channel source audio input180 where

${\delta (n)} = \left\{ {\begin{matrix}1 & : & {n = 0} \\0 & : & {n \neq 0}\end{matrix}.} \right.$

Audio control system 150 can include audio output controller 172 todetermine a set of control parameters that can reformat multi-channelsource audio input 180. Additionally, audio output controller 172determines the set of control parameters based on a frequency energyband representation of each channel of multi-channel source audio input180 and the strength of correlation between each channel ofmulti-channel source audio input 180.

In some examples, audio output controller 172 can utilize a trainedmachine learning model (e.g., trained machine learning model 174) toadjust the parameters of audio signal generator 178 to tune soundcreation from audio signal generator 178. An example of a trainedmachine learning model (e.g., trained machine learning model 174) audiooutput controller 172 can include a neural network type of trainedmachine learning model. Additionally, audio output controller 172 canutilize trained machine learning model 174 to determine the set ofcontrol parameters, based on the frequency energy band representation ofeach channel of multi-channel source audio input 180 and the strength ofcorrelation between each channel of multi-channel source audio input180. For example, audio output controller 172 can utilize the output ofthe Euclidean norm indicating the strength of correlation between eachchannel of multi-channel source audio input 102 and the frequency energyband representation of each channel of multi-channel source audio input180 as inputs for the trained machine learning model to determine theset of control parameters. In some examples, audio output controller 172can further base the set of control parameters based on a set ofdecorrelation parameters determined by correlator resource 166. In otherexamples, audio output controller 172 can utilize trained machinelearning model 174 to determine the set of decorrelation parameters thatthe set of control parameters can be further based on.

A machine learning model can be trained with the output of the machinelearning model. In some examples, the machine learning model can betrained by parameters obtained from a human operator. In some examples,the machine learning model can adjust its own internal parameters tominimize the difference between the estimated set of control parametersand a desired set of control parameters based on, for example, afrequency band energy representation of the multi-channel source audioinput 180 and a strength of correlation between each channel of themulti-channel source audio input 180.

Audio signal generator 178 can utilize a set of control parameters fromaudio output controller 172 to tune sound creation from audio signalgenerator 178 to reflect a set of spatial characteristics (e.g., gainand ITU out) of multi-channel source audio input 180. In some examples,audio signal generator 178 can include hardware components, such asintegrated circuits and/or specialized integrated circuits, and/orsoftware or logic stored on the hardware components (e.g., softwarestored on non-transitory computer-readable medium) to tune soundcreation, based on the set of control parameters.

In some examples, audio control system 150 can include spatial audioparameter module 176. Spatial audio parameter module 176 can reformat aset of spatial parameters of multi-channel source audio input 180 basedon control parameters determined and generated by audio outputcontroller 172. In some examples, the control parameters can specifygains or decorrelation parameters that are to be included in thereformatted set of spatial parameters. Spatial audio parameter module176 can provide to audio signal generator 178 the reformatted set ofspatial parameters along with the corresponding multi-channel sourceaudio input 180.

Audio signal generator 178 can utilize a reformatted set of spatialparameters from spatial audio parameter module 176 to reformatmulti-channel source audio input 102 into a multi-dimensional audiosignal for a mono or stereo audio system. That way a sound created fromthe multi-dimensional audio signal reflects the spatial characteristicsof multi-channel source audio input 180. The multi-dimensional audiosignal can cause the mono or stereo audio system to output a sound thatemulates a distributed or multi-channel sound system that may otherwisebe optimized for multi-channel source audio input 102.

Methodology

FIG. 2 illustrates an example method for reformatting a multi-channelsource audio input into a multi-dimensional audio signal. In belowdiscussion of FIG. 2, reference may be made to reference charactersrepresenting features as shown and described with respect to FIG. 1A forthe purpose of illustrating a suitable component for performing theexample method as being described.

In some examples, audio control system 100 can generate a frequencyenergy band representation of a multi-channel source audio input (200).For example, audio control system 100 can include a first set ofresources (EFB resource 104) to determine and generate a frequencyenergy band representation of each channel of multi-channel source audioinput 102 (e.g., surround-left channel, surround-right channel,front-left channel, front-right channel, rear-center channel and/orfront-center channel).

Additionally, audio control system 100 can determine a valuerepresenting a strength of correlation between each channel of themulti-channel source audio input (202). For example, audio controlsystem 100 can include correlator resource 106 to determine at least avalue representing a strength of correlation between each channel ofmulti-channel source audio input 102.

Based on a value representing a strength of correlation between eachchannel of a multi-channel source audio input and a frequency energyband representation of the multi-channel source audio input, audiocontrol system 100 can determine a set of control parameters for tuningsound creation from audio signal generator 110 (204). In some examples,audio control system 100 can include audio output controller 108 todetermine the set of control parameters, based on a frequency energyband representation of each channel of multi-channel source audio input102 and the value representing the strength of correlation between eachchannel of multi-channel source audio input 102. In some examples, audiooutput controller 108 can utilize a trained machine learning model todetermine the set of control parameters, based on the frequency energyband representation of each channel of multi-channel source audio input102 and the strength of correlation between each channel ofmulti-channel source audio input 102.

Hardware Diagram

FIG. 3 is a block diagram that illustrates a computer system upon whichexamples described herein may be implemented. In one embodiment,computer system 300 may correspond to a mobile computing device, such asa cellular device that is capable of telephony, messaging, and dataservices. Computer system 300 can correspond to a device operated by auser. Examples of such devices include smartphones, handsets, tabletdevices, or in-vehicle computing devices that communicate with cellularcarriers. Computer system 300 includes processor 310, memory resources320, display component 330 (e.g., such as a touch-sensitive displaydevice), communication sub-systems 340 (including wireless communicationsystems), and audio output devices 350 (e.g., speakers). In somevariations, the audio output devices have a single virtual or physicallocation with respect to a housing of the computer system. In othervariations, the audio output devices 350 may have a limited number ofdistribution points, as compared to, for example, a number of channelswhich exist in a multi-channel audio input.

In some examples, communication sub-systems 340 can send and receivecellular data over network(s) 370 (e.g., data channels and voicechannels). Communication sub-systems 340 can include a cellulartransceiver and one or more short-range wireless transceivers. Processor310 can receive multi-channel audio content from an audio source (notillustrated in FIG. 3) that, for example, is linked to the computersystem 300 by network(s) 370, such that multi-channel audio input isreceived by the computer system 300 via communication sub-systems 340.In other examples, the multi-channel audio input can be retrieved frommemory resources 320, or received via a microphone (not shown).

Memory resources 320 can store instructions for a variety of operations.For example, as illustrated in FIG. 3, memory resources 320 can includefrequency energy band instructions 322, strength of correlationinstructions 324 and control parameters instructions 326. Additionally,processor 310 can execute frequency band instructions 322, strength ofcorrelation instructions 324, and control parameters instructions 326 toperform operations for implementing a method, such as described with anexample of FIG. 2. Still further, processor 310 can execute frequencyenergy band instructions 322, strength of correlation instructions 324and control parameters instructions 326 to implement functionality foran audio control system 100, 150, such as described with examples ofFIG. 1A and FIG. 1B. Processor 310 can execute the instructions toreformat multi-channel audio input (e.g., as received via communicationsub-systems 340) into a multi-dimensional output that can, through useof audio output devices 350, create sound that reproduces the spatialcharacteristics of the multi-channel audio input.

Although specific examples have been illustrated and described herein,it will be appreciated by those of ordinary skill in the art that avariety of alternate and/or equivalent implementations may besubstituted for the specific examples shown and described withoutdeparting from the scope of the disclosure. This application is intendedto cover any adaptations or variations of the specific examplesdiscussed herein.

What is claimed is:
 1. An audio control system comprising: a first setof resources to generate a frequency energy band representation of amulti-channel source audio input; a second set of resources to determineat least a value representing a strength of correlation between eachchannel of the multi-channel source audio input; and an audio outputcontroller to determine a set of control parameters for tuning soundcreation from an audio signal generator to reflect a set of spatialcharacteristics of the multi-channel source audio input, based on thefrequency energy band representation and the value.
 2. The audio controlsystem of claim 1, wherein the controller utilizes a machine learningmodel to determine the set of control parameters.
 3. The audio controlsystem of claim 1, wherein the first set of resources includes a bank ofauditory filters.
 4. The audio control system of claim 3, wherein thebank of auditory filters is based on a basilar membrane.
 5. The audiocontrol system of claim 3, wherein an output of each filter in the bankof auditory filters is applied by a root-mean square function.
 6. Theaudio control system of claim 3, wherein, the bank of auditory filtersincludes a first set of auditory filters to be applied to a firstchannel of the multi-channel source audio input.
 7. The audio controlsystem of claim 6, wherein the bank of auditory filters are band-passfilters.
 8. The audio control system of claim 7, wherein the bank ofauditory filters includes at least one of a ⅓-octave filter-bank,⅙th-octave filter-bank, 1/12-th octave filter-bank, critical band filterbank, equivalent rectangular bandwidth, and gammatone filter-bank. 9.The audio control system of claim 1, wherein the second set of resourcesfurther determines a set of decorrelation parameters based on the valuerepresenting a strength of correlation between multiple channels of themulti-channel source audio input.
 10. The audio control system of claim1, wherein the controller further determines a set of decorrelationparameters based on the value representing a strength of correlationbetween multiple channels of the multi-channel source audio input. 11.The audio control system of claim 1, wherein the set of controlparameters includes a parameter for gain and a parameter fordecorrelation.
 12. The audio control system of claim 1, wherein themulti-channel source audio input is in a 5.1 surround sound format. 13.The audio control system of claim 1, wherein the multi-channel sourceaudio input is in a 7.1 surround sound format.
 14. An audio devicecomprising: an audio control component including: a first set ofresources to generate a frequency energy band representation of amulti-channel source audio input; a second set of resources to determineat least a value representing a strength of correlation between eachchannel of the multi-channel source audio input; a controller todetermine a set of control parameters, based on the frequency energyband representation and the value representing a strength of correlationbetween each channel of the multi-channel source audio input; and anaudio signal generator to tuning sound creation to reflect a set ofspatial characteristics of the multi-channel source audio input, basedon the set of control parameters.
 15. A method comprising: generating afrequency energy band representation of a multi-channel source audioinput; determining a first value representing a strength of correlationbetween each channel of the multi-channel source audio input; anddetermining a set of control parameters for tuning sound creation froman audio signal generator, based on the frequency energy bandrepresentation of the multi-channel source audio input and the valuerepresenting a strength of correlation between each channel of themulti-channel source audio input.